prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#! /usr/bin/env python3
import pandas as pd
import pathlib
import fire
import numpy as np
BEDHEADER = [
'chrom',
'start',
'end',
'tr_id',
'score',
'strand',
'thickStart',
'thickEnd',
'itemRgb',
'blockCount',
'blockSizes',
'blockStarts'
]
class Bed(object):
def _... | pd.read_table(tr_type_file) | pandas.read_table |
#!/usr/bin/env python
"""Units and constants for transforming into and out of SI units.
All data is sourced from :py:mod:`scipy.constants` and :py:attr:`scipy.constants.physical_constants`. Every quantity stored in :py:class:`~solarwindpy.core.plasma.Plasma` and contained objects should have a entry in :py:class:`Cons... | pd.Series(_kBoltzmann) | pandas.Series |
from __future__ import print_function
import logging
import pandas as pd
import numpy as np
import scipy.stats as stats
from matplotlib.backends.backend_pdf import PdfPages
import os.path
from .storemanager import StoreManager
from .condition import Condition
from .constants import WILD_TYPE_VARIANT
from .sfmap import ... | pd.isnull(bcm) | pandas.isnull |
from django.shortcuts import render
from django.views.generic import TemplateView
import pandas as pd
from .utils import clean_html
from form_submissions.models import FormResponse
from typeforms.models import Typeform
class DashboardView(TemplateView):
template_name = 'dashboard.html'
def get(self, reques... | pd.DataFrame(answers) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster.bicluster import SpectralCoclustering
from bokeh.plotting import figure, output_file, show
from bokeh.models import HoverTool, ColumnDataSource
from itertools import product
######... | pd.Series([6, 3, 8, 6], index=["q", "w", "e", "r"]) | pandas.Series |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.Timestamp(1) | pandas.Timestamp |
import re
import os
import sys
import pandas as pd
from lxml import etree
from scipy import stats
import gzip
from sqlalchemy import create_engine
class Polite():
"""
MALLET parameters used: 'output-topic-keys', 'output-doc-topics',
'word-topic-counts-file', 'topic-word-weights-file',
'xml-topic-report... | pd.DataFrame(WORD, columns=['word_id', 'word_str']) | pandas.DataFrame |
#!/usr/bin/env python3
from __future__ import print_function
from collections import defaultdict as dd
from collections import Counter
import os
import pysam
import argparse
from operator import itemgetter
import pandas as pd
import numpy as np
import scipy.stats as ss
import matplotlib
# Force matplotlib to not ... | pd.to_numeric(meth_table['loc']) | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 4 10:46:32 2020
@author: OscarFlores-IFi
"""
#%%=========================================================================================================
# Librerías necesarias para correr el código
#==================================... | pd.to_datetime(Fest["Fecha"]) | pandas.to_datetime |
'''
Urban-PLUMBER processing code
Associated with the manuscript: Harmonized, gap-filled dataset from 20 urban flux tower sites
Copyright (c) 2021 <NAME>
Licensed under the Apache License, Version 2.0 (the "License").
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
'''
__title__ =... | pd.DateOffset(minutes=1) | pandas.DateOffset |
# -----------------------------------------------------------------------------
# WSDM Cup 2017 Classification and Evaluation
#
# Copyright (c) 2017 <NAME>, <NAME>, <NAME>, <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the ... | pd.DataFrame(result) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import networkx as nx
def create_polarity_csv(neighbors_csv_path, mcmc_path, user_polarities_paths):
"""
Merge the neighbors csv with both the neighbourhood-based polarities and the
following-based polarities.
Input:
neighbors_csv_path : path... | pd.read_csv(neighbors_csv_path) | pandas.read_csv |
import os
import random
import math
import numpy as np
import pandas as pd
import itertools
from functools import lru_cache
##########################
## Compliance functions ##
##########################
def delayed_ramp_fun(Nc_old, Nc_new, t, tau_days, l, t_start):
"""
t : timestamp
current date
... | pd.Timedelta(days=1) | pandas.Timedelta |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import csv
import hashlib
from typing import ContextManager
import srt
import pandas
import functools
from pydub import AudioSegment
from datetime import datetime, timedelta
from pathlib import Path
from praatio import tgio
from .clean_transcript import clea... | pandas.DataFrame(columns=['wav_filename', 'wav_filesize', 'transcript']) | pandas.DataFrame |
# Copyright 2020 AstroLab Software
# Author: <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | pd.DataFrame.from_dict(data, orient='index') | pandas.DataFrame.from_dict |
import datetime
from datetime import timedelta
from distutils.version import LooseVersion
from io import BytesIO
import os
import re
from warnings import catch_warnings, simplefilter
import numpy as np
import pytest
from pandas.compat import is_platform_little_endian, is_platform_windows
import pandas.util._test_deco... | pd.Timestamp("20130105") | pandas.Timestamp |
# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2021
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hun... | pd.Series(preds) | pandas.Series |
import os
import unittest
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from datetime import datetime
from bqsqoop.utils.parquet_util import ParquetUtil
def sample_df():
_data = [
dict(colA="val1", colB=1),
dict(colA="val2", colB=2)
]
return | pd.DataFrame.from_dict(_data) | pandas.DataFrame.from_dict |
import string
import random
import pathlib
import numpy as np
import pandas as pd
from scipy import stats
path = pathlib.Path(
'~/dev/python/python1024/data/dataproc/006analysis/case').expanduser()
shop_path = path.joinpath('店铺基本数据.xlsx')
# 产品列表
product_list = [f'产品{c}' for c in string.ascii_uppercase]
# 产品价格/成本列... | eries(x_order_prod) | pandas.Series |
from typing import List
from typing import Optional
from typing import Callable
import numpy as np
import pandas as pd
import xarray as xr
from pathlib import Path
from sqlalchemy.orm import Session
import portfolio_management.paths as p
import portfolio_management.data.constants as c
from portfolio_management.io_ut... | pd.DataFrame(records) | pandas.DataFrame |
import numpy.testing as npt
import pandas as pd
import pandas.testing as pdt
import pytest
from message_ix import Scenario, make_df
from message_ix.testing import make_dantzig, make_westeros
def test_make_df():
# DataFrame prepared for the message_ix parameter 'input' has the correct
# shape
result = mak... | pd.DataFrame({"foo": "bar", "baz": [42, 43]}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# ### - PCA and Clustering for Cell painting Level-4 profiles (per dose treament)
#
# #### - Use Silhouette and Davies Bouldin scores to assess the number of clusters from K-Means
# #### - Use BIC scores to assess the number of clusters from Gaussian Mixture Models (GMM)
#
# [r... | pd.read_csv(common_file, sep="\t") | pandas.read_csv |
import ntpath
from datetime import datetime as dt
import os
import pandas as pd
import numpy as np
import math
import sqlite3
# clean the original raw data by storing only the columns that we need, and removing the rest.
def clean(from_path, to_path, columns):
def convert_date(date):
if date == '':
... | pd.notnull(df['Date']) | pandas.notnull |
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 7 22:13:43 2020
@author: <NAME>
"""
#==================================
#ARIMA
#==================================
import os
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import... | pd.to_datetime(df_test.ds) | pandas.to_datetime |
from pickle import TRUE
from flask import *
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
import random
import socket
import os
import time
import rss23
pid = 3
def rssrd(r, xy,client):
f = {}
g = {}
R =... | pd.DataFrame(Origional_test_data) | pandas.DataFrame |
import functools
import numpy as np
from scipy.stats import norm as ndist
import regreg.api as rr
from selection.tests.instance import gaussian_instance
from selection.learning.utils import (partial_model_inference,
pivot_plot,
lee_inference)
fr... | pd.read_csv(csvfile) | pandas.read_csv |
import pandas as pd
filepath_dict = {'yelp': 'data/sentiment_analysis/yelp_labelled.txt',
'amazon': 'data/sentiment_analysis/amazon_cells_labelled.txt',
'imdb': 'data/sentiment_analysis/imdb_labelled.txt'}
df_list = []
for source, filepath in filepath_dict.items():
df = | pd.read_csv(filepath, names=['sentence', 'label'], sep='\t') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 17 19:51:21 2018
@author: Bob
"""
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import DBSCAN
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
from sqlalchemy import create_engine
from c... | pd.read_csv('country_land_data.csv', encoding='latin-1') | pandas.read_csv |
import numpy as np
from scipy.stats import ranksums
import pandas as pd
import csv
file = pd.read_csv('merged-file.txt', header=None, skiprows=0, delim_whitespace=True)
file.columns = ['Freq_allel','dpsnp','sift','polyphen','mutas','muaccessor','fathmm','vest3','CADD','geneName']
df = file.drop_duplicates(keep=False... | pd.read_csv('/encrypted/e3000/gatkwork/COREAD-ESCA-all-driver.tsv', header=None, skiprows=0, sep='\t') | pandas.read_csv |
#########################################################
### DNA variant annotation tool
### Version 1.0.0
### By <NAME>
### <EMAIL>
#########################################################
import pandas as pd
import numpy as np
import allel
import argparse
import subprocess
import sys
import os.path
import pickle... | pd.DataFrame() | pandas.DataFrame |
import preprocess
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import pandas as pd
import plotly.express as px
from sklearn.decomposition import FastICA
import matplotlib.pyplot as plt
import numpy as np
dataPath = r"C:\Users\shalev\Desktop\Introduction_to_AI\Introduction-to-A... | pd.concat([self.reduced_X_for_plot, self.data[['odor']]], axis=1) | pandas.concat |
from dis import dis
import numpy as np
import pandas as pd
import warnings
from credoai.modules.credo_module import CredoModule
from credoai.utils.constants import MULTICLASS_THRESH
from credoai.utils.common import NotRunError, is_categorical
from credoai.utils.dataset_utils import ColumnTransformerUtil
from credoai.ut... | pd.DataFrame(prepared_arr, index=index) | pandas.DataFrame |
#!/usr/bin/env python3.9
import matplotlib.pyplot as plt
import pandas as pd
import subprocess
import copy
import re
import time
import argparse
import sys
class Log:
date=None
add_lines=0
del_lines=0
def reset(self):
self.date=None
self.add_lines=0
self.del_lines=0
def co... | pd.to_datetime(date, unit='s') | pandas.to_datetime |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import pickle
import shutil
import sys
import tempfile
import numpy as np
from numpy import arange, nan
import pandas.testing as pdt
from pandas import DataFrame, MultiIndex, Series, to_datetime
# dependencies testing specific
import pytest
import recordlinka... | DataFrame({'col': ['abc', 'abc', 'abc', 'abc', 'abc']}) | pandas.DataFrame |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.Series([49.2, 55.7, 98.4], index=_index * 3, name='esDisclosurePercentage') | pandas.Series |
import pandas as pd
df = pd.DataFrame({"A": [1, 2, 3, 4, 5]})
s = | pd.Series([1, 2, 3]) | pandas.Series |
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from utils import sum_country_regions, get_change_rates, generate_df_change_rate, sliding_window, generate_COVID_input, generate_COVID_aux_input
import pickle
import copy
import os
import argparse
parser = argparse.ArgumentParse... | pd.read_csv(url_death, error_bad_lines=False) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 5 12:02:47 2021
@author: adarshpl7
"""
import pandas as pd
import numpy as np
# import matplotlib.pyplot as plt
import seaborn as sns
# import math
#Clears console and stored variables
try:
from IPython import get_ipython
get_ipython().mag... | pd.read_csv ("C:/Users/adars/Downloads/Laptop/Semester 4/RA work/Social/SocialIndicators_BroadUS_2020-10-01_2021-03-31/SocialIndicators_BroadUS_OpenToClose_2020-10-01_2021-03-31.tsv", sep = '\t') | pandas.read_csv |
"""
Classes for representing datasets of images and/or coordinates.
"""
from __future__ import print_function
import json
import copy
import logging
import os.path as op
import numpy as np
import pandas as pd
import nibabel as nib
from .base import NiMAREBase
from .utils import (tal2mni, mni2tal, mm2vox, get_template... | pd.merge(id_df, temp_df, left_index=True, right_index=True, how='outer') | pandas.merge |
import os
from typing import Text
from IPython.core.display import display, HTML
from jinja2 import Environment, FileSystemLoader
from numpy.lib.function_base import disp
import pandas as pd
import tensorflow_data_validation as tfdv
from tensorflow_data_validation.utils.display_util import (
get_anomalies_datafram... | pd.DataFrame(meta_table) | pandas.DataFrame |
# -*- coding: utf-8 -*-
''' This program takes a excel sheet as input where each row in first column of sheet represents a document. '''
import pandas as pd
import string
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.clu... | pd.DataFrame(classification_dic, index=[testing_ticket_numbers], columns=['Issue', 'Transformed Data', 'Machine Cluster']) | pandas.DataFrame |
'''
pandas demo -数据清洗 （ numpy-1.19.2 pandas-1.1.2 scikit-learn-0.23.2 ）
'''
import numpy as np
from pandas import Series, DataFrame
import pandas as pd
from sqlalchemy import create_engine
def is_null():
df = pd.DataFrame(np.random.randn(10, 6))
df.iloc[:4, 1] = None
df.iloc[:2, 4:6] = None
df.iloc[6, 3:5] ... | pd.DataFrame(kmodel.cluster_centers_) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
This file is part of the Shotgun Lipidomics Assistant (SLA) project.
Copyright 2020 <NAME> (UCLA), <NAME> (UCLA), <NAME> (UW).
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the Licen... | pd.read_excel(exp_temp_loc, sheet_name='POS', header=0, index_col=None, na_values='.') | pandas.read_excel |
from src.utility.DBUtils import get_engine
from src.model.BaseModel import BaseModel
import pandas as pd
import numpy as np
class Summary:
# use a sqlite database to save and fetch experiment results
def __init__(self, db_path):
self.__engine = get_engine(db_path)
self.table = None
sel... | pd.DataFrame() | pandas.DataFrame |
from secrets import IEX_CLOUD_API_TOKEN as iex_tkn
import pandas as pd
import requests
from statistics import mean
from scipy import stats
import math
stocks_file = 'sp_500_stocks.csv'
api_url = "https://sandbox.iexapis.com/stable/"
def get_info(symbol):
endpt = f"stock/{symbol}/stats?token={iex_tkn}"
respons... | pd.read_csv(filename) | pandas.read_csv |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | tm.assert_frame_equal(result, df) | pandas._testing.assert_frame_equal |
"""
Tests for kf_lib_data_ingest/extract/operations.py
"""
import pandas
import pytest
from kf_lib_data_ingest.common.type_safety import function
from kf_lib_data_ingest.etl.extract import operations
from test_type_safety import type_exemplars
df = pandas.DataFrame({"COL_A": ["1", "2", "3"]})
other_df = pandas.DataF... | pandas.DataFrame({"OUT_COL": ["a", "b", "c"]}) | pandas.DataFrame |
from typing import Union, cast
import warnings
import numpy as np
from pandas._libs.lib import no_default
import pandas._libs.testing as _testing
from pandas.core.dtypes.common import (
is_bool,
is_categorical_dtype,
is_extension_array_dtype,
is_interval_dtype,
is_number,
is_numeric_dtype,
... | is_interval_dtype(right.dtype) | pandas.core.dtypes.common.is_interval_dtype |
import os
import pandas as pd
from tqdm import tqdm
import pipelines.p1_orca_by_stop as p1
from utils import constants, data_utils
NAME = 'p2_aggregate_orca'
WRITE_DIR = os.path.join(constants.PIPELINE_OUTPUTS_DIR, NAME)
def load_input():
path = os.path.join(constants.PIPELINE_OUTPUTS_DIR, f'{p1.NAME}.csv')
... | pd.read_csv(path) | pandas.read_csv |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2020/10/19 9:28
Desc: 新浪财经-A股-实时行情数据和历史行情数据(包含前复权和后复权因子)
"""
import re
import json
import demjson
from py_mini_racer import py_mini_racer
import pandas as pd
import requests
from tqdm import tqdm
from akshare.stock.cons import (zh_sina_a_stock_payload,
... | pd.to_datetime(temp_df["date"]) | pandas.to_datetime |
#!/usr/bin/env python
#
# -----------------------------------------------------------------------------
# Copyright (c) 2018 The Regents of the University of California
#
# This file is part of kevlar (http://github.com/dib-lab/kevlar) and is
# licensed under the MIT license: see LICENSE.
# ----------------------------... | pandas.DataFrame(columns=colnames) | pandas.DataFrame |
import sys,os
import pandas as pd
import numpy as np
from statsmodels.tsa.api import ARIMA, SARIMAX, ExponentialSmoothing, VARMAX
from statsmodels.tsa.arima.model import ARIMA as StateSpaceARIMA
import unittest
from nyoka import ExponentialSmoothingToPMML, StatsmodelsToPmml
class TestMethods(unittest.TestCase):
... | pd.Series(data, index) | pandas.Series |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from seir.sampling.model import SamplingNInfectiousModel
import logging
logging.basicConfig(level=logging.INFO)
if __name__ == '__main__':
logging.info('Loading data')
# read calibration data
actual_hospitalis... | pd.to_datetime('2020-03-27') | pandas.to_datetime |
import numpy as np
import pandas as pd
from relation import *
from connection import *
class RuleSet:
#############################
# Methods to build rule set #
#############################
def __init__(self,rules_list):
''' @rules_list: list of ordered dictionnaries each representing a rule... | pd.concat([self.set,rule],sort=False) | pandas.concat |
from time import time
from datetime import datetime
import os, sys
import numpy as np
from scipy.stats.mstats import gmean
import scipy.spatial.distance as ssd
import scipy.cluster.hierarchy as hc
import pandas as pd
import pickle
import gensim, data_nl_processing, data_nl_processing_v2
import spacy
import scispacy
fr... | pd.read_csv(data_path2) | pandas.read_csv |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import inspect
import pandas as pd
pd.options.display.colheader_justify = 'right'
| pd.set_option('display.unicode.east_asian_width', True) | pandas.set_option |
#This class defines the Scoreboard data structure and its associated methods
import pandas as pd
import os
from dotenv import load_dotenv
import boto3
class Scoreboard:
standard_display = ["Member","Score"]
all_time_display = ["Member","AllTime"]
commits_display = ["Member","Commits"]
#Load up S3 a... | pd.read_csv(obj["Body"], index_col=0) | pandas.read_csv |
# This scripts are possible solutions for the Database Tasks
# Packages that might have to be installed via pip/conda:
# conda install pandas
# conda install MySQL-python
# conda install mysqlclient
# conda install pymongo
# conda install sqlite3
# General Packages
import pandas as pd
# Packages for MySQL
... | pd.read_sql(query, conn) | pandas.read_sql |
import unittest
import pytest
from pyalink.alink import *
def print_value_and_type(v):
print(type(v), v)
class TestAkStream(unittest.TestCase):
def setUp(self) -> None:
self.lfs = LocalFileSystem()
self.hfs = HadoopFileSystem("2.8.3", "hdfs://xxx:9000")
self.ofs = OssFileSystem("3... | pd.DataFrame(arr) | pandas.DataFrame |
import argparse as ap
from itertools import product
from typing import List, Tuple
import bnet.utype as ut
import numpy as np
import yaml
from nptyping import NDArray
from pandas.core.frame import DataFrame
class Config(dict):
def __init__(self, path: str):
f = open(path, "r")
self.__path = path
... | DataFrame(result, columns=columns) | pandas.core.frame.DataFrame |
#!/usr/bin/env python3
import gc
import os
import pickle
import fire
import h5py
import matplotlib.pyplot as plt
import seaborn as sns
from hyperopt.fmin import generate_trials_to_calculate
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import precision_recall_curve
from numpy import linalg as L... | pd.concat(temp, ignore_index=True) | pandas.concat |
"""
<NAME>017
Variational Autoencoder - Pan Cancer
scripts/vae_pancancer.py
Usage:
Run in command line with required command arguments:
python scripts/vae_pancancer.py --learning_rate
--batch_size
--epochs
... | pd.read_table(rnaseq_file, index_col=0) | pandas.read_table |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | StringIO(s) | pandas.compat.StringIO |
import streamlit as st
from alphapept.gui.utils import (
check_process,
init_process,
start_process,
escape_markdown,
)
from alphapept.paths import PROCESSED_PATH, PROCESS_FILE, QUEUE_PATH, FAILED_PATH
from alphapept.settings import load_settings_as_template, save_settings
import os
import psutil
import... | pd.DataFrame(failed_files) | pandas.DataFrame |
"""
Helper functions for loading, converting, reshaping data
"""
import pandas as pd
import json
from pandas.api.types import CategoricalDtype
FILLNA_VALUE_CAT = 'NaN'
CATEGORICAL = "categorical"
CONTINUOUS = "continuous"
ORDINAL = "ordinal"
COLUMN_CATEGORICAL = 'categorical_columns'
COLUMN_CONTINUOUS = 'continuous... | CategoricalDtype(categories=col['i2s'], ordered=True) | pandas.api.types.CategoricalDtype |
from numbers import Number
from collections import Iterable
import re
import pandas as pd
from pandas.io.stata import StataReader
import numpy as np
pd.set_option('expand_frame_repr', False)
class hhkit(object):
def __init__(self, *args, **kwargs):
# if input data frame is specified as a stata data file or text ... | pd.Series(right_using_on_key) | pandas.Series |
#IMPORTING LIBRARIES
import numpy as np
import pandas as pd
import os
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from statistics import mean
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
from skl... | pd.read_csv('../input/indian-liver-patient-records/indian_liver_patient.csv') | pandas.read_csv |
from typing import Tuple
import warnings
warnings.simplefilter("ignore", UserWarning)
from functools import partial
from multiprocessing.pool import Pool
import pandas as pd
import numpy as np
import numpy_groupies as npg
from cellphonedb.src.core.core_logger import core_logger
from cellphonedb.src.core.models.comple... | pd.concat([interactions_data_result, mean_pvalue_result], axis=1, join='inner', sort=False) | pandas.concat |
"""
Active Fairness Run through questions
"""
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.calibration import _Si... | pd.DataFrame(self.dataset_test.labels[:, 0]) | pandas.DataFrame |
#!/usr/bin/env python3 -u
# -*- coding: utf-8 -*-
__author__ = ["<NAME>"]
__all__ = [
"TEST_YS",
"TEST_SPS",
"TEST_ALPHAS",
"TEST_FHS",
"TEST_STEP_LENGTHS_INT",
"TEST_STEP_LENGTHS",
"TEST_INS_FHS",
"TEST_OOS_FHS",
"TEST_WINDOW_LENGTHS_INT",
"TEST_WINDOW_LENGTHS",
"TEST_INITI... | pd.Timedelta(-3, unit="D") | pandas.Timedelta |
import pytest
import numpy as np
import pandas
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
import matplotlib
import modin.pandas as pd
from modin.pandas.utils import to_pandas
from numpy.testing import assert_array_equal
from .utils import (
random_state,
RAND_LOW,
RAND_... | pandas.DataFrame([]) | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
import pickle
import json
para = {
"window_size": 1,
"step_size": 0.5,
"structured_file": "BGL.log_structured.csv",
"BGL_sequence_train": "BGL_sequence_train.csv",
"BGL_sequence_test": "BGL_sequence_test.csv"
}
def load_BGL():
... | pd.DataFrame(columns=['sequence', 'label']) | pandas.DataFrame |
"""
Unit and regression test for the kissim.comparison.measures module.
"""
import pytest
import numpy as np
import pandas as pd
from kissim.comparison.utils import (
format_weights,
scaled_euclidean_distance,
scaled_cityblock_distance,
)
@pytest.mark.parametrize(
"feature_weights, feature_weights_f... | pd.Series([4, 3]) | pandas.Series |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import json
import datetime
import os
from os import listdir
from os.path import isfile, join
from shutil import copyfile
import logging
import pandas as pd
logger = logging.getLogger("root")
logging.basicConfig(
format="\033[1;36m%(levelname)s: %(filename)s (def %(f... | pd.read_csv(target) | pandas.read_csv |
###############################################################################
# Copyright (c) 2021, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory
# Written by <NAME> <<EMAIL>>
#
# All rights reserved.
#
# Permission is hereby granted, free of charge, to any person... | pd.DataFrame(data=synth_data, columns=self.column_names) | pandas.DataFrame |
"""Run unit tests.
Use this to run tests and understand how tasks.py works.
Setup::
mkdir -p test-data/input
mkdir -p test-data/output
mysql -u root -p
CREATE DATABASE testdb;
CREATE USER 'testusr'@'localhost' IDENTIFIED BY 'test<PASSWORD>';
GRANT ALL PRIVILEGES ON testdb.* TO 'te... | pd.read_sql_table(tblname, sql_engine) | pandas.read_sql_table |
"""
utilities that are helpful in general model building
"""
import clickhouse_driver
from muti import chu
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.io as pio
import scipy.stats as stats
import math
import os
def r_square(yh, y):
"""
find the r-square for the model i... | pd.Series(binary_variable) | pandas.Series |
"""
Tests that apply specifically to the Python parser. Unless specifically
stated as a Python-specific issue, the goal is to eventually move as many of
these tests out of this module as soon as the C parser can accept further
arguments when parsing.
"""
import csv
from io import BytesIO, StringIO
import py... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
#Imports
import requests
import json
import os
import sys
import pandas as pd
import numpy as np
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash import Dash
from dash.dependencies import Output, Input, State
from dash.exceptions import PreventUpda... | pd.read_json(j_data, orient='split') | pandas.read_json |
"""Transformer for datetime data."""
import numpy as np
import pandas as pd
from pandas.core.tools.datetimes import _guess_datetime_format_for_array
from rdt.transformers.base import BaseTransformer
from rdt.transformers.null import NullTransformer
class UnixTimestampEncoder(BaseTransformer):
"""Transformer for ... | pd.Series(datetime_data) | pandas.Series |
import numpy as np
import pandas as pd
from numpy.testing import assert_array_equal
from pandas.testing import assert_frame_equal
from nose.tools import (assert_equal,
assert_almost_equal,
raises,
ok_,
eq_)
from rsmtool.p... | assert_frame_equal(df_new, df_new_expected) | pandas.testing.assert_frame_equal |
# ========== (c) <NAME> 3/8/21 ==========
import logging
import pandas as pd
import numpy as np
import plotly.express as px
logger = logging.getLogger(__name__)
root_logger = logging.getLogger()
root_logger.setLevel(logging.INFO)
sh = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(... | pd.set_option('display.max_columns', 20) | pandas.set_option |
#Creo el dataset para la predicción del boosting
import gc
gc.collect()
import pandas as pd
import seaborn as sns
import numpy as np
#%% marzo
marzo = pd.read_csv(r'C:\Users\argomezja\Desktop\Data Science\MELI challenge\Project MELI\Dataset_limpios\marzo_limpio.csv.gz')
marzo = marzo.loc[marzo['day']>=4].r... | pd.merge(final, subtest7, left_index=True, right_index=True) | pandas.merge |
import unittest
from abc import ABC
import numpy as np
import pandas as pd
from toolbox.ml.ml_factor_calculation import ModelWrapper, calc_ml_factor, generate_indexes
from toolbox.utils.slice_holder import SliceHolder
class MyTestCase(unittest.TestCase):
def examples(self):
# index includes non trading... | pd.Timestamp(year=2010, month=1, day=1) | pandas.Timestamp |
from __future__ import division #brings in Python 3.0 mixed type calculations
import numpy as np
import os
import pandas as pd
import sys
#find parent directory and import model
parentddir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
sys.path.append(parentddir)
from base.uber_model impo... | pd.Series([], dtype="float") | pandas.Series |
import os
import copy
import pytest
import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import feather as pf
from pyarrow import parquet as pq
from time_series_transform.io.base import io_base
from time_series_transform.io.numpy import (
from_numpy,
to_numpy
)
from time_series_transfor... | pd.testing.assert_frame_equal(x,expectedX,check_dtype=False) | pandas.testing.assert_frame_equal |
import glob
import numpy as np
import pandas as pd
from collections import OrderedDict
#from . import metrics
import metrics
from .csv_reader import csv_node
__all__ = ['tune_threshold',
'assemble_node',
'assemble_dev_threshold',
'metric_reading',
'Ensemble']
def tune_thres... | pd.DataFrame(df_dict) | pandas.DataFrame |
import re
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""],... | pd.Series([1, 2, 3]) | pandas.Series |
import datetime
import hashlib
import os
import time
from warnings import (
catch_warnings,
simplefilter,
)
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
Series,
Timestamp,
concat,
date_range,
timedelt... | tm.assert_series_equal(obj, objs[leaf]) | pandas._testing.assert_series_equal |
# ============================================================================
# Piotroski f score implementation (data scraped from yahoo finance)
# Author - <NAME>
# Please report bugs/issues in the Q&A section
# =============================================================================
import requests
from bs4... | pd.DataFrame(financial_dir_cy) | pandas.DataFrame |
# -*- coding: utf-8 -*-
#!/usr/bin/python3
__author__ = "<NAME>"
__copyright__ = "Copyright 2019-2022"
__license__ = "MIT"
__version__ = "0.1.0"
__maintainer__ = "<NAME>, <NAME>"
__email__ = "<EMAIL>"
__status__ = "Dev"
import textwrap
import pandas as pd
import numpy as np
import json
import argparse
... | pd.read_csv('Ribosomes_newlist_F.txt', sep='\t') | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2020/3/21 0021
# @Author : justin.郑 <EMAIL>
# @File : index_baidu.py
# @Desc : 获取百度指数
import json
import urllib.parse
import pandas as pd
import requests
def decrypt(t: str, e: str) -> str:
"""
解密函数
:param t:
:type t:
:param e:
... | pd.DataFrame(age_list) | pandas.DataFrame |
# Importing Data in Python (Part 1) on Data Camp
#######################################
# Part 1: Introduction and flat files
#######################################
## Importing entire text files
# Open a file: file
file = open('moby_dick.txt', mode='r')
# Print it
print(file.read())
# Check whether file is cl... | pd.DataFrame.hist(data[['Age']]) | pandas.DataFrame.hist |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from joblib import Memory
import datetime
from azure_table_interface import query_aq_data
# Set up caching for the Azure table access
memory = Memory('./_cache_')
ID_to_name = {'nesta-1': 'Priory Rd (South)',
'nesta-2': 'Priory Rd ... | pd.concat(dfs, axis=1) | pandas.concat |
#Se omiten tildes para evitar inconvenientes de codificacion
#Librerias requeridas
import sqlite3
from sqlite3 import Error
import pandas as pd
import numpy as np
import sys
import random
#Definir separador para la carga de los archivos CSV
separador = ";"
#Funciones para generacion de campos aleatorios... | pd.read_csv(rutaTablaCSV,sep=separador) | pandas.read_csv |
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2017 by University of Kassel and Fraunhofer Institute for Wind Energy and
# Energy System Technology (IWES), Kassel. All rights reserved. Use of this source code is governed
# by a BSD-style license that can be found in the LICENSE file.
from math import pi
from numpy impo... | DataFrame(ppc_net['branch'][:, [0, 1, 8, 9]]) | pandas.DataFrame |
import pandas as pd
import numpy as np
def get_series(data: (pd.Series, pd.DataFrame), col='close') -> pd.DataFrame:
"""
Get close column from intraday data
Args:
data: intraday data
col: column to return
Returns:
pd.Series or pd.DataFrame
"""
if isinstance(data, pd.S... | pd.DataFrame(data) | pandas.DataFrame |
"""Tools used for clustering analysis"""
import csv
__author__ = "<NAME> (http://www.vmusco.com)"
import numpy
import os
import pandas
from mlperf.clustering.clusteringtoolkit import ClusteringToolkit
class DatasetFacts:
"""Object alternative to method read_dataset"""
def __init__(self, data):
self... | pandas.DataFrame(initial_clusters) | pandas.DataFrame |
# Object Oriented Programming Examples
import pandas as pd
df = | pd.DataFrame(['tree frog', 'white rhino', 'zebra']) | pandas.DataFrame |
import math
import glob
import os
import uuid
import itertools
import pandas as pd
import numpy as np
import datetime as dt
class GSTools(object):
@staticmethod
def load_csv_files(dir_str):
'''
This function reads all csv from the given directory, stores them in a dictionary and returns it.
... | pd.to_datetime(df['date'], format='%Y-%m-%d %H:%M:%S') | pandas.to_datetime |
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